- Table View
- List View
Data Analytics in Finance (Data Analytics Applications)
by Huijian DongData Analytics in Finance covers the methods and application of data analytics in all major areas of finance, including buy-side investments, sell-side investment banking, corporate finance, consumer finance, financial services, real estate, insurance, and commercial banking. It explains statistical inference of big data, financial modeling, machine learning, database querying, data engineering, data visualization, and risk analysis. Emphasizing financial data analytics practices with a solution- oriented purpose, it is a “one-stop-shop” of all the major data analytics aspects for each major finance area.The book paints a comprehensive picture of the data analytics process including: Statistical inference of big data Financial modeling Machine learning and AI Database querying Data engineering Data visualization Risk analysis Each chapter is crafted to provide complete guidance for many subject areas including investments, fraud detection, and consumption finance. Avoiding data analytics methods widely available elsewhere, the book focuses on providing data analytics methods specifically applied to key areas of finance. Written as a roadmap for researchers, practitioners, and students to master data analytics instruments in finance, the book also provides a collection of indispensable resources for the readers’ reference. Offering the knowledge and tools necessary to thrive in a data-driven financial landscape, this book enables readers to deepen their understanding of investments, develop new approaches to risk management, and apply data analytics to finance.
Data Analytics in Marketing, Entrepreneurship, and Innovation (Data Analytics Applications)
by Mounir Kehal Shahira El AlfyInnovation based in data analytics is a contemporary approach to developing empirically supported advances that encourage entrepreneurial activity inspired by novel marketing inferences. Data Analytics in Marketing, Entrepreneurship, and Innovation covers techniques, processes, models, tools, and practices for creating business opportunities through data analytics. It features case studies that provide realistic examples of applications. This multifaceted examination of data analytics looks at: Business analytics Applying predictive analytics Using discrete choice analysis for decision-making Marketing and customer analytics Developing new products Technopreneurship Disruptive versus incremental innovation The book gives researchers and practitioners insight into how data analytics is used in the areas of innovation, entrepreneurship, and marketing. Innovation analytics helps identify opportunities to develop new products and services, and improve existing methods of product manufacturing and service delivery. Entrepreneurial analytics facilitates the transformation of innovative ideas into strategy and helps entrepreneurs make critical decisions based on data-driven techniques. Marketing analytics is used in collecting, managing, assessing, and analyzing marketing data to predict trends, investigate customer preferences, and launch campaigns.
Data Analytics in Power Markets
by Yi Wang Qixin Chen Hongye Guo Kedi ZhengThis book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.
Data Analytics in Project Management (Data Analytics Applications)
by Seweryn SpalekThis book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book’s integrated approach to investigating both fields enhances the value of research findings.
Data Analytics in System Engineering: Proceedings of 7th Computational Methods in Systems and Software 2023, Vol. 3 (Lecture Notes in Networks and Systems #910)
by Radek Silhavy Petr SilhavyThese proceedings offer an insightful exploration of integrating data analytics in system engineering. This book highlights the essential role of data in driving innovation, optimizing processes, and solving complex challenges in the field. Targeted at industry professionals, researchers, and enthusiasts, this book serves as a comprehensive resource, providing actionable insights and showcasing transformative applications of data in engineering. It is a must-read for anyone keen on understanding and participating in the ongoing evolution of system engineering in our data-centric world.
Data Analytics in System Engineering: Proceedings of 7th Computational Methods in Systems and Software 2023, Vol. 4 (Lecture Notes in Networks and Systems #935)
by Radek Silhavy Petr SilhavyThese proceedings offer an insightful exploration of integrating data analytics in system engineering. This book highlights the essential role of data in driving innovation, optimizing processes, and solving complex challenges in the field. Targeted at industry professionals, researchers, and enthusiasts, this book serves as a comprehensive resource, providing actionable insights and showcasing transformative applications of data in engineering. It is a must-read for anyone keen on understanding and participating in the ongoing evolution of system engineering in our data-centric world.
Data Analytics in e-Learning: Approaches and Applications (Intelligent Systems Reference Library #220)
by Marian Cristian MihăescuThis book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications. This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.
Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS
by Joe MinichinoA comprehensive and accessible roadmap to performing data analytics in the AWS cloud In Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS, accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you’ll explore every relevant aspect of data analytics—from data engineering to analysis, business intelligence, DevOps, and MLOps—as you discover how to integrate machine learning predictions with analytics engines and visualization tools. You’ll also find: Real-world use cases of AWS architectures that demystify the applications of data analytics Accessible introductions to data acquisition, importation, storage, visualization, and reporting Expert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify maintenanceA can't-miss for data architects, analysts, engineers and technical professionals, Data Analytics in the AWS Cloud will also earn a place on the bookshelves of business leaders seeking a better understanding of data analytics on the AWS cloud platform.
Data Analytics in the Era of the Industrial Internet of Things
by Aldo DagninoThis book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts’ decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision-making in the IIoT enterprise. The book starts by defining an IIoT enterprise and the framework used to efficiently operate. A description of the concepts of industrial analytics, which is a major engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role in increasing the competitiveness of industrial enterprises that operate using the IIoT technology and business concepts. Real world examples of data driven IIoT enterprises and various business models are presented and a discussion on how the use of ML and data science help address complex decision-making problems and generate new job opportunities. The book presents in an easy-to-understand manner how ML algorithms work and operate on data generated in the IIoT enterprise. Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.
Data Analytics with Hadoop: An Introduction for Data Scientists
by Jenny Kim Benjamin BengfortReady to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce.Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data.Understand core concepts behind Hadoop and cluster computingUse design patterns and parallel analytical algorithms to create distributed data analysis jobsLearn about data management, mining, and warehousing in a distributed context using Apache Hive and HBaseUse Sqoop and Apache Flume to ingest data from relational databasesProgram complex Hadoop and Spark applications with Apache Pig and Spark DataFramesPerform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib
Data Analytics-Based Demand Profiling and Advanced Demand Side Management for Flexible Operation of Sustainable Power Networks (Springer Theses)
by Jelena PonoćkoThis thesis deals with two important and very timely aspects of the future power system operation - assessment of demand flexibility and advanced demand side management (DSM) facilitating flexible and secure operation of the power network. It provides a clear and comprehensive literature review in these two areas and states precisely the original contributions of the research. The book first demonstrates the benefits of data mining for a reliable assessment of demand flexibility and its composition even with very limited observability of the end-users. It then illustrates the importance of accurate load modelling for efficient application of DSM and considers different criteria in designing DSM programme to achieve several objectives of the network performance simultaneously. Finally, it demonstrates the importance of considering realistic assumptions when planning and estimating the success of DSM programs.The findings presented here have both scientific and practical significance; they gained her BSc and MSc degrees in electrical engineering from the University of Belgrade in 2011 and 2012 respectively. She graduated with her PhD from the University of Manchester. She has presented at several conferences, and has won runner-up prizes in poster presentation at three. She has authored or co-authored more than 40 journal, conference and technical papers.provide a basis for further research, and can be used to guide future applications in industry.
Data Analytics: 31st British International Conference on Databases, BICOD 2017, London, UK, July 10–12, 2017, Proceedings (Lecture Notes in Computer Science #10365)
by Andrea Calì, Peter Wood, Nigel Martin and Alexandra PoulovassilisThis book constitutes the refereed conference proceedings of the 31st British International Conference on Databases, BICOD 2017 - formerly known as BNCOD (British National Conference on Databases) - held in London, UK, in July 2017.The 17 revised full papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics such as data cleansing, data integration, data wrangling, data mining and knowledge discovery, graph data and knowledge graphs, intelligent data analysis, approximate and flexible querying, data provenance and ontology-based data access. They are organized in the following topical sections: data wrangling and data integration; data analysis and data mining; graph data querying and analysis; multidimensional data and data quality; and distributed and multimedia data management.
Data Analytics: A Theoretical and Practical View from the EDISON Project
by Yuri Demchenko Juan J. Cuadrado-GallegoBuilding upon the knowledge introduced in The Data Science Framework, this book provides a comprehensive and detailed examination of each aspect of Data Analytics, both from a theoretical and practical standpoint. The book explains representative algorithms associated with different techniques, from their theoretical foundations to their implementation and use with software tools.Designed as a textbook for a Data Analytics Fundamentals course, it is divided into seven chapters to correspond with 16 weeks of lessons, including both theoretical and practical exercises. Each chapter is dedicated to a lesson, allowing readers to dive deep into each topic with detailed explanations and examples. Readers will learn the theoretical concepts and then immediately apply them to practical exercises to reinforce their knowledge. And in the lab sessions, readers will learn the ins and outs of the R environment and data science methodology to solve exercises with the R language. With detailed solutions provided for all examples and exercises, readers can use this book to study and master data analytics on their own. Whether you're a student, professional, or simply curious about data analytics, this book is a must-have for anyone looking to expand their knowledge in this exciting field.
Data Analytics: Concepts, Techniques, and Applications
by Al-Sakib Khan Pathan Mohiuddin AhmedLarge data sets arriving at every increasing speeds require a new set of efficient data analysis techniques. Data analytics are becoming an essential component for every organization and technologies such as health care, financial trading, Internet of Things, Smart Cities or Cyber Physical Systems. However, these diverse application domains give rise to new research challenges. In this context, the book provides a broad picture on the concepts, techniques, applications, and open research directions in this area. In addition, it serves as a single source of reference for acquiring the knowledge on emerging Big Data Analytics technologies.
Data Analytics: Effective Methods for Presenting Results (Data Analytics Applications)
by Subhashish Samaddar Satish NargundkarIf you are a manager who receives the results of any data analyst’s work to help with your decision-making, this book is for you. Anyone playing a role in the field of analytics can benefit from this book as well. In the two decades the editors of this book spent teaching and consulting in the field of analytics, they noticed a critical shortcoming in the communication abilities of many analytics professionals. Specifically, analysts have difficulty in articulating in business terms what their analyses showed and what actionable recommendations were made. When analysts made presentations, they tended to lapse into the technicalities of mathematical procedures, rather than focusing on the strategic and tactical impact and meaning of their work. As analytics has become more mainstream and widespread in organizations, this problem has grown more acute. Data Analytics: Effective Methods for Presenting Results tackles this issue. The editors have used their experience as presenters and audience members who have become lost during presentation. Over the years, they experimented with different ways of presenting analytics work to make a more compelling case to top managers. They have discovered tried and true methods for improving presentations, which they share. The book also presents insights from other analysts and managers who share their own experiences. It is truly a collection of experiences and insight from academics and professionals involved with analytics. The book is not a primer on how to draw the most beautiful charts and graphs or about how to perform any specific kind of analysis. Rather, it shares the experiences of professionals in various industries about how they present their analytics results effectively. They tell their stories on how to win over audiences. The book spans multiple functional areas within a business, and in some cases, it discusses how to adapt presentations to the needs of audiences at different levels of management.
Data Analytics: Models and Algorithms for Intelligent Data Analysis
by Thomas A. RunklerThis book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.
Data Analytics: Models and Algorithms for Intelligent Data Analysis - A Comprehensive Introduction
by Thomas A. RunklerThis book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. The new edition integrates themes such as Word Embeddings, Transformer Models, and generative AI among the contents and offers new exercises in addition. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.
Data Analytics: Proceedings Of 4th Conference On Sustainable Urban Mobility (csum2018), 24 - 25 May, Skiathos Island, Greece (Advances In Intelligent Systems and Computing #879)
by Eftihia G. Nathanail Ioannis D. KarakikesThis book aims at showing how big data sources and data analytics can play an important role in sustainable mobility. It is especially intended to provide academicians, researchers, practitioners and decision makers with a snapshot of methods that can be effectively used to improve urban mobility. The different chapters, which report on contributions presented at the 4th Conference on Sustainable Urban Mobility, held on May 24-25, 2018, in Skiathos Island, Greece, cover different thematic areas, such as social networks and traveler behavior, applications of big data technologies in transportation and analytics, transport infrastructure and traffic management, transportation modeling, vehicle emissions and environmental impacts, public transport and demand responsive systems, intermodal interchanges, smart city logistics systems, data security and associated legal aspects. They show in particular how to apply big data in improving urban mobility, discuss important challenges in developing and implementing analytics methods and provide the reader with an up-to-date review of the most representative research on data management techniques for enabling sustainable urban mobility
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)
by Liang Xu Seon Ki ParkThis book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV)
by Liang Xu Seon Ki ParkThis book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including adaptive observations, sensitivity analysis, parameter estimation and AI applications. The book is useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
Data Augmentation, Labelling, and Imperfections: Second MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings (Lecture Notes in Computer Science #13567)
by Hien V. Nguyen Yuan Xue Sharon X. HuangThis book constitutes the refereed proceedings of the Second MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022.DALI 2022 accepted 12 papers from the 22 submissions that were reviewed. The papers focus on rigorous study of medical data related to machine learning systems.
Data Augmentation, Labelling, and Imperfections: Third MICCAI Workshop, DALI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings (Lecture Notes in Computer Science #14379)
by Chen Chen Chao Chen Yuan Xue Lianrui Zuo Yihao LiuThis LNCS conference volume constitutes the proceedings of the 3rd International Workshop on Data Augmentation, Labeling, and Imperfections (DALI 2023), held on October 12, 2023, in Vancouver, Canada, in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 16 full papers together in this volume were carefully reviewed and selected from 23 submissions. The conference fosters a collaborative environment for addressing the critical challenges associated with medical data, particularly focusing on data, labeling, and dealing with data imperfections in the context of medical image analysis.
Data Capital: How Data is Reinventing Capital for Globalization
by Chunlei TangThis book defines and develops the concept of data capital. Using an interdisciplinary perspective, this book focuses on the key features of the data economy, systematically presenting the economic aspects of data science. The book (1) introduces an alternative interpretation on economists’ observation of which capital has changed radically since the twentieth century; (2) elaborates on the composition of data capital and it as a factor of production; (3) describes morphological changes in data capital that influence its accumulation and circulation; (4) explains the rise of data capital as an underappreciated cause of phenomena from data sovereign, economic inequality, to stagnating productivity; (5) discusses hopes and challenges for industrial circles, the government and academia when an intangible wealth brought by data (and information or knowledge as well); (6) proposes the development of criteria for measuring regulating data capital in the twenty-first century for regulatory purposes by looking at the prospects for data capital and possible impact on future society. Providing the first a thorough introduction to the theory of data as capital, this book will be useful for those studying economics, data science, and business, as well as those in the financial industry who own, control, or wish to work with data resources.
Data Center Handbook
by Hwaiyu GengData Center Handbook provides the fundamentals, technologies, and best practices in designing, constructing and managing mission critical, energy efficient data centers.* The most comprehensive single source guide ever published in this field, with 36 chapters and over 350 illustrations written by 50 world class authors* Offers disaster management techniques and lessons learned from 2011 earthquake and tsunami in Japan, and 2012 Superstorm Sandy* Discusses international standards and requirements, with contributions from experts in the United States, Canada, United Kingdom, France, Sweden, Japan, Korea, and China
Data Center Handbook: Plan, Design, Build, and Operations of a Smart Data Center
by Hwaiyu GengDATA CENTER HANDBOOK Written by 59 experts and reviewed by a seasoned technical advisory board, the Data Center Handbook is a thoroughly revised, one-stop resource that clearly explains the fundamentals, advanced technologies, and best practices used in planning, designing, building and operating a mission-critical, energy-efficient, sustainable data center. This handbook, in its second edition, covers anatomy, ecosystem and taxonomy of data centers that enable the Internet of Things and artificial intelligent ecosystems and encompass the following:SECTION 1: DATA CENTER OVERVIEW AND STRATEGIC PLANNINGMegatrends, the IoT, artificial intelligence, 5G network, cloud and edge computingStrategic planning forces, location plan, and capacity planning Green design & construction guidelines and best practicesEnergy demand, conservation, and sustainability strategiesData center financial analysis & risk managementSECTION 2: DATA CENTER TECHNOLOGIESSoftware-defined environmentComputing, storage, network resource managementWireless sensor networks in data centersASHRAE data center guidelinesData center telecommunication cabling, BICSI and TIA 942Rack-level and server-level coolingCorrosion and contamination controlEnergy saving technologies and server designMicrogrid and data centersSECTION 3: DATA CENTER DESIGN & CONSTRUCTIONData center site selectionArchitecture design: rack floor plan and facility layoutMechanical design and cooling technologiesElectrical design and UPSFire protectionStructural designReliability engineeringComputational fluid dynamicsProject managementSECTION 4: DATA CENTER OPERATIONS TECHNOLOGIESBenchmarking metrics and assessmentData center infrastructure managementData center air managementDisaster recovery and business continuity managementThe Data Center Handbook: Plan, Design, Build, and Operations of a Smart Data Center belongs on the bookshelves of any professionals who work in, with, or around a data center.